WebQTL
 
   |    Home    |    Search    |    Help    |    News    |    References    |    Policies    |    Links    |    Welcome! Login   

Data Set Group: All Tissues, RNA-Seq GTEx modify this page

Data Set: GTEx Human Adrenal Gland (Mar14) RPKM Log2 modify this page
GN Accession: GN545
GEO Series: GSE45878
Title: The Genotype-Tissue Expression (GTEx) project
Organism: Human (hg19)
Group: GTEx
Tissue: None
Dataset Status: Public
Platforms: Affymetrix Human Gene 1.0 ST Array custom CDF (HuGene11stv1_Hs_ENSG.cdf v14.1.0)
Normalization: RPKM
Contact Information
Kristin Ardlie
The Broad Institute of MIT and Harvard
7 Cambridge Center
Cambridge, MA 02142 USA
Tel. 617-714-7000
questions@broadinstitute.org
Website
Download datasets and supplementary data files

Specifics of this Data Set:
None

Summary:

The Genotype-Tissue Expression (GTEx) project is a collaborative effort that aims to identify correlations between genotype and tissue-specific gene expression levels that will help identify regions of the genome that influence whether and how much a gene is expressed. GTEx is funded through the Common Fund, and managed by the NIH Office of the Director in partnership with the National Human Genome Research Institute, National Institute of Mental Health, the National Cancer Institute, the National Center for Biotechnology Information at the National Library of Medicine, the National Heart, Lung and Blood Institute, the National Institute on Drug Abuse, and the National Institute of Neurological Diseases and Stroke, all part of NIH. This series of 837 samples represents multiple tissues collected from 102 GTEX donors and 1 control cell line. In total, 30 tissue sites are represented including Adipose, Artery, Heart, Lung, Whole Blood, Muscle, Skin, and 11 brain subregions. RNA-seq expression data, robust clinical data, pathological annotations, and genotypes are also available for these samples from dbGaP (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v2.p1) and the GTEx portal (www.broadinstitute.org/gtex). While GTEx is no longer generating Affymetrix expression data, donor enrollment continues and is expected to reach 1,000 by the end of 2015. Updates to the GTEx data in dbGaP and the GTEx Portal will be made periodically. contributor: GTEx Laboratory, Data Analysis, and Coordinating Center (LDACC) contributor: The Broad Institute of MIT and Harvard (LDACC PIs: Kristin Ardlie and Gaddy Getz).

WU-Minn HCP Consortium Open Access Data Use Terms



About the cases used to generate this set of data:

The Genotype-Tissue Expression project (GTEx) aims to create a comprehensive public atlas of gene expression and regulation across multiple human tissues. The resource will provide valuable insights in to the mechanisms of gene regulation, aid in the interpretation of genome wide association studies, and enable studies of expression quantitative trait loci (eQTLs), alternative splicing, and the tissue specificity of gene regulatory mechanisms.

The GTEx project recently completed an initial pilot phase during which >185 donor DNAs were genotyped using high density SNP and exome arrays. RNA expression was profiled on multiple tissues from these donors (from 9 to 30) by both array-based methods and RNA sequencing, to an average depth of 50 million reads. These pilot phase data have been made available to the public through davailable through the database of Genotype and Phenotype (dbGaP).

For more information about the GTEx project, please visit the About GTEx page, view the Consortium Members, or read the Publication Policy. Additional GTEx resources such as funding opportunities and information for donors are also available on the NIH Common Fund and NHGRI websites respectively. 



About the tissue used to generate this set of data:

GTEx explore all tissues:

GTEx explore all tissues



About the array platform:

Expression

RPKM data are used as produced by RNA-SeQC. Filter on >=10 individuals having >0.1RPKM. Log and quantile normalize the expression values across all samples. Outlier correction: for each gene, rank values across samples then map to a standard normal.



About data values and data processing:

Analysis Methods

Preprocessing

RNA-seq

RNA-seq was performed using the Illumina TruSeq library construction protocol. This is a non-strand specific polyA+ selected library. The sequencing produced 76-bp paired end reads.

See also:How to Evaluate and Use Human and Mouse mRNA Data Sets (e.g. GTEx)

Alignment to the HG19 human genome was performed using Tophat v1.1.4 assisted by the GENCODE v12 transcriptome definition. In a post processing step, unaligned reads are reintroduced into the bam. The final bam contains aligned and unaligned reads, marked duplicates, quality score recalibration. It should be noted that Tophat produces multiple mappings for some reads, but in post processing one read is flagged as the primary alignment.

Genotyping

DNA samples that are sent to the Broad Institute Genetic Analysis Platform for genotyping, are placed on 96-well plates using the Illumina HumanOmni5-4v1_B SNP array. Omni genotypes are called using GenomeStudio v2010.3 with the calling algorithm/genotyping module version 1.8.4 using the default cluster file HumanOmni5-4v1-Multi_B.egt. Called genotypes are run through a standard QC pipeline and only samples passing a call rate threshold of 97%, and passing genetic fingerprint and gender concordance are passed. For the final eQTL analysis, the following filters were applied: call rate (< 90%), low HWE (pValue < 1E-6) or are monormorphic.

Expression Quantification

Gene/Transcript Model

Gencode Version 12
Contig names modified to match the reference genome used for alignment
Procedure for collapsing transcript model into gene model

Primary source: gencode.v12
List exons as a set of intervals, discarding any labeled as 'retained_intron' and retaining only coding and linc rna.
Create a separate bin for other types of transcripts and process them independently.
Merge overlapping intervals.
Discard intervals associated with multiple genes.
Map intervals back to gene identifiers and output in GTF format.
Quantification

For gene/exon level read count and gene level RPKM values, we filter reads based on the requirements:

Reads must have be uniquely mapped (for tophat this is mapping quality > 3; == 255).
Reads must have proper pairs.
Alignment distance must be <=6.
Reads must be contained 100% within exon boundaries. Reads overlapping introns are not counted.
Exon

For exon read counts, if a read overlaps multiple exons, then then a fractional value equal to the portion of the read contained within that exon is allotted.

Transcript

Transcript-level quantification is provided by Flux Capacitor.

eQTL Analysis

QC and Sample Exclusion Process

D statistic outliers are removed.
Gender-specific expression outliers are removed.
Samples with less than 10 million mapped reads are removed.
In the case of replicates, the samples with the greater number of reads are chosen.
Covariates

3 Genotyping PCs.
15 Peer factors:


The input to PEER are the post-normalization expression values described below.
Gender.
Expression

RPKM data are used as produced by RNA-SeQC.
Filter on >=10 individuals having >0.1RPKM.
Log and quantile normalize the expression values across all samples.
Outlier correction: for each gene, rank values across samples then map to a standard normal.
Genotypes

Imputation-based genotypes:
Call Rate Threshold 95%.
Info score Threshold 0.4.
Minor Allele Frequency >= 5%.
Sex chromosomes have been excluded excluded.
Matrix eQTL Parameters

Produced for radius +-1mb from TSS.
P value threshold set to 1 to emit all p-values.
Storey FDR

The Storey q-value method was applied using the public R package with default values.
eQTLs were filtered for an FDR <=5%.
Tissues

There are 9 Tissues that have sufficient sample numbers (n > 80).

Adipose_Subcutaneous
Artery_Tibial
Heart_Left_Ventricle
Lung
Muscle_Skeletal
Nerve_Tibial
Skin_Sun_Exposed_Lower_leg
Thyroid
Whole_Blood

Note: RPKM original values that we enter in GeneNetwork have been log2 transformed after added 1, then values lower than 2.0 were transformed to 0 (zero).

Read Bits of DNA blog: GTEx is throwing away 90% of their data and response to: "GTEx is throwing away 90% of their data. by Manolis Dermitzakis, Gad Getz, Krisitn Ardlie, Roderic Guigo for the GTEx consortium.

See also: How to Evaluate and Use Human and Mouse mRNA Data Sets (e.g. GTEx)



Notes:


Experiment Type:

GTEx samples are collected from deceased donors at low post-mortem intervals and preserved in PAXgene fixative prior to DNA and RNA extraction.



Contributor:

Please review and cite: John Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, Gary Walters, Fernando Garcia, Nancy Young, Barbara Foster, Mike Moser, Ellen Karasik, Bryan Gillard, Kimberley Ramsey, Susan Sullivan, Jason Bridge, Harold Magazine, John Syron, Johnelle Fleming, Laura Siminoff, Heather Traino, Maghboeba Mosavel, Laura Barker, Scott Jewell, Dan Rohrer, Dan Maxim, Dana Filkins, Philip Harbach, Eddie Cortadillo, Bree Berghuis, Lisa Turner, Eric Hudson, Kristin Feenstra, Leslie Sobin, James Robb, Phillip Branton, Greg Korzeniewski, Charles Shive, David Tabor, Liqun Qi, Kevin Groch, Sreenath Nampally, Steve Buia, Angela Zimmerman, Anna Smith, Robin Burges, Karna Robinson, Kim Valentino, Deborah Bradbury, Mark Cosentino, Norma Diaz-Mayoral, Mary Kennedy, Theresa Engel, Penelope Williams, Kenyon Erickson, Kristin Ardlie, Wendy Winckler, Gad Getz, David DeLuca, Daniel MacArthur, Manolis Kellis, Alexander Thomson, Taylor Young, Ellen Gelfand, Molly Donovan, Yan Meng, George Grant, Deborah Mash, Yvonne Marcus, Margaret Basile, Jun Liu, Jun Zhu, Zhidong Tu, Nancy J Cox, Dan L Nicolae, Eric R Gamazon, Hae Kyung Im, Anuar Konkashbaev, Jonathan Pritchard, Matthew Stevens, Timothèe Flutre, Xiaoquan Wen, Emmanouil T Dermitzakis, Tuuli Lappalainen, Roderic Guigo, Jean Monlong, Michael Sammeth, Daphne Koller, Alexis Battle, Sara Mostafavi, Mark McCarthy, Manual Rivas, Julian Maller, Ivan Rusyn, Andrew Nobel, Fred Wright, Andrey Shabalin, Mike Feolo, Nataliya Sharopova, Anne Sturcke, Justin Paschal, James M Anderson, Elizabeth L Wilder, Leslie K Derr, Eric D Green, Jeffery P Struewing, Gary Temple, Simona Volpi, Joy T Boyer, Elizabeth J Thomson, Mark S Guyer, Cathy Ng, Assya Abdallah, Deborah Colantuoni, Thomas R Insel, Susan E Koester, A Roger Little, Patrick K Bender, Thomas Lehner, Yin Yao, Carolyn C Compton, Jimmie B Vaught, Sherilyn Sawyer, Nicole C Lockhart, Joanne Demchok & Helen F Moore. Nature Genetics 45, 580–585 (2013).



Citation:


Data source acknowledgment:


Study Id:
176

CITG WWW service initiated January, 1994 as The Portable Dictionary of the Mouse Genome and June 15, 2001 as WebQTL. This site is currently operated by Rob Williams, Lei Yan, Pjotr Prins, Zachary Sloan, Arthur Centeno. Design and code by Lei Yan, Zach Sloan, Kenneth Manly, Jintao Wang, Danny Arends, Pjotr Prins, Sam Ockman, Xiaodong Zhou, Christian Fernandez, Ning Liu, Rudi Alberts, Elissa Chesler, Evan G. Williams, Alexander G. Williams, Robert W. Williams, and colleagues. Python Powered Registered with Nif
GeneNetwork support from:
    Join GeneNetwork Mailing List
    It took 0.103 second(s) for lily.uthsc.edu to generate this page